Self-calibrating contactless power consumption sensing

ABSTRACT

A system for sensing electrical power usage in an electrical power infrastructure of a structure. The system can include a sensing device configured to be attached to a panel of the circuit breaker box overlying at least part of the one or more main electrical power supply lines. The system also can include a calibration device configured to be electrically coupled to the electrical power infrastructure of the structure. The system further can include one or more processing modules configured to receive one or more output signals from the sensing device. The sensing device can be devoid of being electrically or physically coupled to the one or more main electrical power supply lines or the electrical power infrastructure when the sensing device is attached to the panel. Other embodiments are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/567,561, filed Sep. 25, 2009, now U.S. Pat. No. 8,930,152,and a continuation-in-part of U.S. patent application Ser. No.14/457,032, filed Aug. 11, 2014, now U.S. Pat. No. 9,594,098, which is adivisional application of U.S. patent application Ser. No. 13/175,770,filed Jul. 1, 2011, now U.S. Pat. No. 8,805,628, which is acontinuation-in-part of U.S. patent application Ser. No. 12/567,561,filed Sep. 25, 2009, now U.S. Pat. No. 8,930,152, and claims the benefitof U.S. Provisional Application No. 61/380,174, filed Sep. 3, 2010, andU.S. Provisional Application No. 61/361,296, filed Jul. 2, 2010. U.S.patent application Ser. Nos. 12/567,561, 13/175,770, and 14/457,032, andU.S. Provisional Application Nos. 61/380,174 and 61/361,296 areincorporated herein by reference in their entirety.

TECHNICAL FIELD

This disclosure relates generally to sensing electric power consumption,and relates more particularly to self-calibrating, contactless powerconsumption sensing.

BACKGROUND

Energy conservation and eco-feedback research continues to be a focus inthe Ubiquitous Computing (Ubicomp) and Human-Computer Interaction (HCI)communities. Given that 28% of U.S. energy consumption is directlycontributed by household activities, the home is a natural place tostudy. Yet obtaining whole-home power consumption information inreal-time by homeowners or even researchers can be a challenging task.For instance, certain smart meters provide data at 15 minutes intervals,yet gaining access to that information can be difficult due toclosed-source and often private protocols and application interfaces.One common approach is to install commercially available currenttransformers (CTs) inside the breaker panel. Safely installing CTs,however, requires hiring a trained electrician as it involves placing asensor around the main electrical feed in the breaker panel. Mostresearchers and homeowners do not have the training or confidence to dosuch an installation. In fact, the National Electric Code (NEC) hasstrict rules on the requirement of professional installation of CTs. Inaddition, certain states in the United States altogether prohibit CTsfrom being installed inside the breaker panel, in which case analternative is to use an expensive pass-through meter. The pass-throughmeter requires involvement of the utility company, as an end-user cannottamper with or alter the installation of an electricity meter.

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided in which:

FIG. 1 illustrates a view of an exemplary system attached to a circuitbreaker and electrical power infrastructure of a structure, according toa first embodiment;

FIG. 2 illustrates a block diagram of the system of FIG. 1;

FIG. 3 illustrates a block diagram an exemplary system, according to asecond embodiment;

FIG. 4 illustrates an exemplary graph of a function, which can bederived by placing a predicted function a region of the function,according to an embodiment;

FIG. 5 illustrates an exemplary graph of a function, according to anembodiment;

FIG. 6 illustrates an exemplary graph of a function, according to anembodiment;

FIG. 7 illustrates an exemplary graph of a function, according to anembodiment;

FIG. 8 (top) illustrates an exemplary graph of magnetic flux for outputsignals generated by magnetic field sensors and FIG. 8 (bottom)illustrates an exemplary graph of a corresponding predicted currentwaveform through one leg;

FIG. 9 (top) illustrates an exemplary graph of a predicted currentwaveform and a measured voltage waveform, and FIG. 9 (bottom)illustrates an exemplary graph of magnetic flux for output signalsgenerated by magnetic field sensors that were used to predict thecurrent waveform;

FIG. 10 (top) illustrates an exemplary graph of a predicted currentwaveform and a measured voltage waveform, and FIG. 10 (bottom)illustrates an exemplary graph of magnetic flux for output signalsgenerated by magnetic field sensors that were used to predict thecurrent waveform;

FIG. 11 illustrates exemplary graphs showing a transfer function and itsdecomposed elements;

FIG. 12 illustrates a view of the system of FIG. 1 attached to circuitbreaker and electrical power infrastructure, showing various sensorplacement positions;

FIG. 13 illustrates a flow chart for a method of sensing electricalpower being provided to a structure using a sensing device, acalibration device, and one or more processing modules, according toanother embodiment;

FIG. 14 illustrates a flow chart for a method of training the neuralnetwork model upon sensing the triggering event, according to theembodiment of FIG. 13;

FIG. 15 illustrates a flow chart for a sensing electrical power beingprovided to a structure using a sensing device, a calibration device,and one or more processing modules, according to another embodiment;

FIG. 16 illustrates a front elevational view of a computer system thatis suitable for implementing an embodiment of the systems of FIGS. 1-3;and

FIG. 17 illustrates a representative block diagram of an example of theelements included in the circuit boards inside a chassis of the computersystem of FIG. 16.

For simplicity and clarity of illustration, the drawing figuresillustrate the general manner of construction, and descriptions anddetails of well-known features and techniques may be omitted to avoidunnecessarily obscuring the present disclosure. Additionally, elementsin the drawing figures are not necessarily drawn to scale. For example,the dimensions of some of the elements in the figures may be exaggeratedrelative to other elements to help improve understanding of embodimentsof the present disclosure. The same reference numerals in differentfigures denote the same elements.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.Furthermore, the terms “include,” and “have,” and any variationsthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, system, article, device, or apparatus that comprises alist of elements is not necessarily limited to those elements, but mayinclude other elements not expressly listed or inherent to such process,method, system, article, device, or apparatus.

The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,”“under,” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is to be understood that the terms soused are interchangeable under appropriate circumstances such that theembodiments of the apparatus, methods, and/or articles of manufacturedescribed herein are, for example, capable of operation in otherorientations than those illustrated or otherwise described herein.

The terms “couple,” “coupled,” “couples,” “coupling,” and the likeshould be broadly understood and refer to connecting two or moreelements mechanically and/or otherwise. Two or more electrical elementsmay be electrically coupled together, but not be mechanically orotherwise coupled together. Coupling may be for any length of time,e.g., permanent or semi-permanent or only for an instant. “Electricalcoupling” and the like should be broadly understood and includeelectrical coupling of all types. The absence of the word “removably,”“removable,” and the like near the word “coupled,” and the like does notmean that the coupling, etc. in question is or is not removable.“Mechanical coupling” and the like should be broadly understood andinclude mechanical coupling of all types.

The absence of the word “removably,” “removable,” and the like near theword “coupled,” and the like does not mean that the coupling, etc. inquestion is or is not removable.

As defined herein, two or more elements are “integral” if they arecomprised of the same piece of material. As defined herein, two or moreelements are “non-integral” if each is comprised of a different piece ofmaterial.

As defined herein, “approximately” can, in some embodiments, mean withinplus or minus ten percent of the stated value. In other embodiments,“approximately” can mean within plus or minus five percent of the statedvalue. In further embodiments, “approximately” can mean within plus orminus three percent of the stated value. In yet other embodiments,“approximately” can mean within plus or minus one percent of the statedvalue.

DESCRIPTION OF EXAMPLES OF EMBODIMENTS

Various embodiments include a system for sensing electrical power usagein an electrical power infrastructure of a structure. The structure caninclude a circuit breaker box and one or more main electrical powersupply lines for the electrical power infrastructure of the structure.The system can include a sensing device configured to be attached to apanel of the circuit breaker box overlying at least part of the one ormore main electrical power supply lines. The sensing device can includeone or more magnetic field sensors. The system also can include acalibration device configured to be electrically coupled to theelectrical power infrastructure of the structure. The calibration devicecan include a load unit. The system further can include one or moreprocessing modules configured to receive one or more output signals fromthe sensing device. The sensing device can be devoid of beingelectrically or physically coupled to the one or more main electricalpower supply lines or the electrical power infrastructure when thesensing device is attached to the panel. The one or more processingmodules can be further configured to determine the electrical powerusage when the sensing device is coupled at any location over the panel,based at least in part on the one or more output signals received fromthe sensing device.

Several embodiments include a method of sensing electrical power beingprovided to a structure using a sensing device, a calibration device,and one or more processing modules. The sensing device can include oneor more magnetic field sensors. The sensing device can be attached to apanel of a circuit breaker box. The panel of the circuit breaker box canoverlie at least a part of one or more main electrical power supplylines for an electrical power infrastructure of a structure. Thecalibration device can include a load unit. The calibration device canbe electrically coupled to the electrical power infrastructure of thestructure. The method can include automatically calibrating the sensingdevice by determining a first transfer function in a piecewise mannerbased on a plurality of ordinary power consumption changes in thestructure over a first time period. The method also can includedetermining a power consumption measurement using the one or moreprocessing modules based on one or more output signals of the sensingdevice and the first transfer function.

Several embodiments can include a method of sensing electrical powerbeing provided to a structure using a sensing device, a calibrationdevice, and one or more processing modules. The sensing device can beattached to a panel of a circuit breaker box. The panel of the circuitbreaker box can overlie at least a part of one or more main electricalpower supply lines for an electrical power infrastructure of astructure. The calibration device can include a load unit. The methodcan include determining a current flowing in the one or more mainelectrical power supply lines based at least in part on one or moreoutput signals of the sensing device. The method also can includedetermining a phase difference between the current flowing in the one ormore main electrical power supply lines and a voltage measured by thecalibration device. The calibration devices can be electrically coupledto the electrical power infrastructure of the structure. The sensingdevice can include one or more magnetic field sensors configured tomeasure a magnetic flux produced by at least a part of the one or moremain electrical power supply lines and generate the one or more outputsignals of the sensing device based on the magnetic flux measured by thesensing device. The sensing device can be devoid of being electricallyor physically coupled to the one or more main electrical power supplylines.

Contactless power consumption sensors have been used to reduce suchdeployment burdens by offering “stick on” sensors that go on the outsideof the breaker panel. That technique has utilized magnetic sensors tosense the magnetic field induced by the 60 Hertz (Hz) current flowingthrough the main lines inside the breaker panel. That existing approachwas a step towards simple and easy to deploy non-intrusive powermonitoring, but there can be some limitations to consider. First, theexisting approach can require the user to precisely position the sensoron the panel, which is a difficult task for an end-user to perform.Second, the existing approach can assume a linear transfer functionbetween the magnetic sensors and the current, which limits its accuracyto a small current range. Third, the existing approach does notnecessarily take into account the small fields generated by the variousbranch circuits that may reside in the area directly behind the magneticsensors. Fourth, the existing approach can infer apparent power, but notnecessarily true (real) power, because it does not take into account thephase information between the voltage and current waveforms. By notbeing able to determine true power, the existing approaches might notable to accurately infer power use of highly inductive loads, such ascompact fluorescent lamps (CFL), light emitting diodes (LEDs), heating,ventilation, and air conditioning (HVAC) systems, computers, televisions(TVs), etc., which now tend to constitute much of the power consumptionin a modern home. In addition, researchers in the energy disaggregationcommunity have limited utility with just the apparent power data.

Prior technologies have utilized a plug-in calibrator, but with theassumption that the plug-in calibrator would draw known power loads tofit a transfer function. One possible drawback of such an approach,however, is that it can assume the calibrator is able to draw a largerange of loads, such as between 0 and 20 kilowatts (kW), depending onthe size of the home and types of appliances present. It can beimpractical for a plug-in calibrator to draw such large loads because ofsafe heat-dissipation limitations, as well as the difficulty ofconstructing such a device in a small form factor.

There are many commercially available sensors for measuring and showingappliance level energy use at each outlet, such as the ConserveInsight™, GreenSwitch, and Kill-A-Watt™ products. In the case ofwhole-house power consumption measurement, some of the popularcommercially available solutions are The Energy Detective (TED®) and thePowerCost Monitor products. Installing TED products involves placing aCT around the main electrical feeds (mains) inside the breaker panel,which requires a professional installation due to high-voltage shockhazard. On the other hand, PowerCost products can be easily installed bya homeowner without hiring an electrician, but can require eitherelectromechanical meters or electronic meters with an exposed andcompatible optical port. Hence, it can be constrained to specific typesof meters with its update rate, as well as performance dependent on themeter and its exposed data ports.

Because of such limitations, contactless solutions are emerging that tryto infer power without having direct access to the mains. One suchapproach measures the current at individual circuit breakers using amagnetic sensor placed on the face of the breaker switch itself. Butmost electric codes do not allow anything to be placed on the circuitbreakers for extended use because of the potential interference with itslife-saving cutoff operation. In addition, such an approach wouldrequire a sensor to be placed on each circuit breaker to gather wholehome power use or on the main circuit breaker, if present. In a similarmagnetic field-based approach, a magnetic sensor is required to beplaced on every breaker switch on the panel. In addition to requiringseveral sensors, such an approach also needs to be calibrated manuallyby the homeowner, which can be extremely difficult and/or impracticalfor a homeowner to perform.

Another approach uses a pair of magnetic sensors placed on the face ofthe breaker panel (instead of the breakers) to sense the current flowingthrough the main bus bars. That approach utilized a set of LEDs to helpguide the user in the placement of the sensors. That approach also useda load calibrator to create a transfer function, but assumed a lineartransfer function and that the calibrator could emulate the entire powerrange of the house. Despite the use of LEDs to help with placement,other branch circuits and stray wires can impact the magnetic fieldunder the sensors. Moreover, the state of the magnetic flux changesthroughout the day as various appliances are used, which means that theLEDs are most helpful when the breaker panel state remains the sameafter the initial installation and are least helpful when the breakerpanel state changes significantly after the initial installation.Further, that approach inferred apparent power, but did not take intoaccount the phase angle between current and reference voltage.

Turning to the drawings, FIG. 1 illustrates a view of an exemplarysystem 100 attached to a circuit breaker 190 and electrical powerinfrastructure 160 of a structure, according to a first embodiment. FIG.2 illustrates a block diagram of system 100, according to the firstembodiment. System 100 is merely exemplary and is not limited to theembodiments presented herein. System 100 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. System 100 can be considered a system for sensing electricalpower usage in electrical power infrastructure 160 of the structure. Forexample, the structure can be any building that is provided with adistinct electrical service and/or serves a designated purpose. Examplesof structures include single family residences, apartments,condominiums, townhouses, duplexes, triplexes, quadraplexes, and soforth, as well as commercial structures such as businesses, warehouses,and factories—to list but a few by way of example, but without anyintended or implied limitation.

In some embodiments, system 100 can include: a sensing device 110, adisplay device 120, and/or a calibration device 180. In many examples,system 100 can be used on a conventional breaker box or circuit breakerpanel, such as circuit breaker panel 190. Circuit breakers panels in theUnited States generally comply with the General Electric “style” basedon the guidelines from National Electrical Manufacturers Association(NEMA). Specifically, circuit breaker panels generally have a frontsurface with an access door. The front surface generally covers theinterior where main electrical feeds or lines are connected to a busbar. As shown in FIG. 1, circuit breaker panel 190 can includeindividual circuit breakers, such as individual circuit breaker 165 andindividual circuit breaker 161. In various embodiments, circuit breakerpanel 190 can include panel 196 with an exterior surface. In severalembodiments, circuit breaker panel 190 can include a door 197 thatprovides access to panel the individual circuit breakers (e.g., 161,165) and/or panel 196. In a number of embodiments, circuit breaker panel190 can include one or more main circuit breakers (not shown). In manyembodiments, circuit breaker panel 190 can include at least a portion ofmain electrical power conductors 193, 194, and 195.

In various embodiments, the individual circuit breaker (e.g., 161, 165)can include circuit breaker levers (e.g., circuit breaker levers 162 and166, respectively), and can provide electrical power through branchcircuits, such as branch circuits 163 and 167, respectively. Forexample, branch circuit 163 can provide electrical power to receptacle164 of the structure, and/or branch circuit 167 can provide electricalpower to receptacle 168 of the structure. In several embodiments,electrical power infrastructure 160 of the structure can include atleast circuit breaker panel 190, main electrical power conductors 193,194, and 195 (“feeds”/“legs”), and the branch circuits in the structure,such as branch circuits 163 and 167. In many examples, main electricalpower conductors 193, 194, and 195 can be electrically coupled toattachment bar 198, and/or connected to one or more bus bars, such asbus bar 199, which can supply electrical power to the individual circuitbreakers (e.g., 161, 165) and to the branch circuits (e.g., 163, 167) inelectrical power infrastructure 160. In many embodiments, panel 196 canoverlie at least part of main electrical power conductors 193, 194, and195 and associated circuitry to protect people from inadvertentlycontacting these energized electrical power conductors. Panel 196 can becomposed of steel or another metal. In many examples, door 197 coversthe individual circuit breakers (e.g., 161, 165), and is typicallyclosed for aesthetic reasons but can be opened to allow access to thelevers (e.g., 162, 166) of the individual circuit breakers (e.g., 161,165) within circuit breaker panel 190.

In many embodiments, system 100 can be used to compute the currentconsumption in the structure by inferring the current being drawnthrough the main electrical power conductors 193, 194, and 195. Ingeneral, residential and small commercial electrical service istypically 240 volt (V) split phase service. This refers to the utilityproviding two 120 V alternating current (AC) source conductors (e.g.,power conductors 193 and 195) that are 180 degrees out of phase, alongwith a neutral conductor (e.g., power conductor 194) that can be used toreturn current from either power conductor 193 or 194. Power conductors193, 194, and 195 can be the “feeder” or “main” electrical powerconductors that carry the incoming power from the utility before beingsplit up into branch circuits (e.g., 163, 167) that serve differentloads within the structure. The 120 V loads can primarily includelower-wattage loads, i.e., loads plugged into standard 3-prong 120 V 15ampere (A) or 120 V 20 A outlets, and small appliances with less thanapproximately 2 kilowatt (kW) power draw. These loads can be wired inindividual circuits and attached to receptacles, such as receptacles 164and 168, and can flow between main electrical power conductors 193 and194 pair (the “first phase branch” or “first leg”), or between mainelectrical power conductors 195 and 194 pair (the “second phase branch”or “second leg.”) The 240 V loads are typically large appliances (e.g.,electric dryer, stove, air conditioner compressor, electric baseboardheaters) that consume more than two kW (kilowatts). In this case, theload current flows between power conductors 193 and 195 and no loadcurrent flows in power conductor 194. Because of the 180 degree phaserelationship between the voltages on power conductors 193 and 195, thetotal voltage is 240 V.

Industrial buildings typically have three-phase service where the threephases are 120 degrees offset from each other. Although a single phasepower service has been described above, system 100 can also be used witha three phase power service as well. In either case, system 100 canpredict the current flowing through all the legs (e.g., main electricalpower conductors 193, 194, 195). By sensing the magnetic fieldsgenerated by main electrical power conductors 193, 194, and 195, system100 can sense the total current drawn by all loads from the utilitybecause all loads in the structure are coupled in parallel to powerconductors 193, 194, and/or 195. The field generated from the main legs(e.g., 193, 194, 195) can be used to estimate the current flowingthrough each leg (e.g., 193, 194, 195) separately, which radiates a fewcentimeters from the wire (e.g., 193, 194, 195) and even through thelayer of sheet metal of panel 196. In the ideal situation, magneticfield would scale linearly with the current. The relationship betweenmagnetic field and current, however, is not as simple in practicebecause of fields from all neighboring wires, reflected magnetic fields,and magnetic nonlinearities of the sheet metal.

Still referring to FIGS. 1-2, in many embodiments sensing device 110 caninclude one or more magnetic field sensors, such as magnetic fieldsensors 111, 112, 113, and 114. Magnetic field sensors 111-114 caninclude an inductive pickup, a Hall effect sensor, a magnetoresistivesensor, or any other type of sensor configured to respond to the timevarying magnetic field produced by the conductors inside circuit breakerpanel 190. For example, magnetic field sensors 111-114 can be aRadioShack removable telephone pickup sensors, model number 07C12.Magnetic field sensors 111-114 can be placed on the exterior surface ofpanel 196 to detect the magnetic field radiated from the 60 Hz currentflowing in main electrical power conductors 193, 194, and 195 behindpanel 196, as well as some of the branch circuits (e.g., 163, 167). FIG.1 shows a sample placement of sensing device 110 on panel 196. In someembodiments, the magnetic field sensors (e.g., 111-114) can be printedas an array of inductors using ubiquitous circuit printing techniques,which can allow sensing device 110 to be attached to circuit breakerpanel 190 as a sticker.

In several embodiments, sensing device 110 can include one or moremagnets 117. In a number of embodiments, magnets 117 can be permanentmagnets, such that magnets 117 can create a persistent magnetic field.In various embodiments, the one or more magnetic field sensors (e.g.,111-114) can be surrounded by magnets 117. In various embodiments, themagnets 117 can be placed to reduce the effect of magnetic nonlinearityof the sheet metal of panel 196 located in between main electrical powerconductors 193, 194, and 195, and magnetic field sensors 111-114. Thetime difference between the actual current waveform flowing in mainelectrical power conductors 193, 194, and/or 195, and the magneticwaveform sensed by magnetic field sensors 111-114 can be dependent uponthe magnetic saturation and permeability of the material of panel 196.Saturating the magnetic field with magnets 117 can reduce thenonlinearity induced by the sheet metal if panel 196. In other words,the nonlinearity of the phase difference between the actual and sensedwaveform can be reduced by surrounding magnetic field sensors 111-114with magnets 117, which beneficially result in more accurate predictionsof the phase angle calculation, as discussed below in further detail.

In a number of embodiments, sensing device 110 can include an attachmentmechanism 219. Attachment mechanism 219 can be configured to attachsensing device 110 to a surface of circuit breaker panel 190, such aspanel 196. In some examples, attachment mechanism 219 can include anadhesive, a hook-and-loop material, a magnet, or another attachmentmechanism.

In various embodiments, sensing device 110 can include a transmitter 215and/or power source 216, which can be used to transmit one or moresignals for the magnetic fields sensed by magnetic field sensors111-114. For example, transmitter 215 can be a wired or wirelesschannel. For example, transmitter 215 can communicate using acommunication protocol, such as Wi-Fi (wireless fidelity, the IEEE(Institute of Electrical and Electronics Engineers) 802.11 standard),Zigbee (IEEE 802.15.4), Bluetooth (IEEE 802.15.1), or another suitableprotocol, such as a proprietary data communication protocol. In someembodiments, power source 216 can be a battery or other suitable powersource, and can provide electrical power for transmission throughtransmitter 215.

Still referring to FIGS. 1-2, in many embodiments, calibration device180 can include an electrical plug 282, which can be plugged into areceptacle in the structure, such as receptacle 164 or 168, and whichcan allow calibration device to be electrically coupled to a branchcircuit, such as branch circuit 161 and/or branch circuit 167. In someembodiments, system 100 can include a single calibration device 180. Inother embodiments, system 100 can include more than one calibrationdevice, such as calibration device 180. For example, a first calibrationdevice (e.g., 180) can be electrically coupled on the first phase branch(first leg) of electrical power infrastructure 160 and a secondcalibration device (e.g., 180) can be electrically coupled on the secondphase branch (second leg) of electrical power infrastructure 160. Inmany embodiments, calibration device 284 can include a transceiver 284,which can be used to receive communications to control calibrationdevice 180. For example, transceiver 284 can be a wired or wirelesschannel, and/or can communicate using a communication protocol, such asWi-Fi, ZigBee, Bluetooth, or another suitable protocol.

In many embodiments calibration device 180 can include a load controlunit 283 and a load unit 281. Load unit 281 can include one or morecalibration loads and/or one or more switches. The switches can bemechanical relay switches, solid state relays, triacs, transistors(e.g., field effect transistors (FETs), silicon-controlled rectifiers(SCRs), bipolar junction transistors (BJTs), insulated-gate bipolartransistors (IGBTs), etc.), or another suitable controllable switchingdevice. Through the use of the switches, the one or more calibrationloads can be temporarily electrically coupled to a branch circuit (e.g.,163 or 167) of electrical power infrastructure 160 of the structure tofacilitate calibration of sensor 110 and/or system 100.

The calibration loads in load unit 281 can be one or more resistorsand/or one or more reactive loads, such as an inductor or capacitor withor without a resistive component. Additionally, the calibration load canbe a load with a variable resistance. As an example, the calibrationloads can be four high wattage resistors, such as Ohmite chassis mountresistors, part number TGHLVR100JE, which can be connected in seriesand/or parallel combinations via the switches.

In a number of embodiments, load control unit 283 can include amicrocontroller to receive communications from transceiver 284 and/orcan send signals to the switches of load unit 281 to drive the relays.The switching signal can be used to temporarily complete a branchcircuit (e.g., 163, 167) and switch one or more calibration loads on tocomplete a circuit and draw power through main electrical powerconductors 193, 194, and/or 195. For example, the load control unit candrive the switches to provide 25 watt (W), 100 W, 200 W, and/or 300 Wloads. The one or more calibration devices (e.g., 180) can draw one or aseries of known loads to automatically calibrate the sensing device 110and/or system 100. In a number of embodiments, the maximum load that canbe drawn by calibration device 180 is 1000 W. In another embodiment, themaximum load that can be drawn by calibration device 180 is 300 W. Inyet other embodiments, the maximum load that can be drawn by calibrationdevice 180 is 50 W. The relatively small maximum loads drawn bycalibration device 180 can beneficially allow calibration device tosafely dissipate heat, reduce power consumption, and/or be provided in asmall form factor. In many embodiments, system 100 can advantageouslyleverage the actual normal electrical activities occurring in thestructure to pulls up to only 300 W through calibration device 180, butnonetheless, can calibrate sensor 110 and/or system 100 over the entirerange of possible power usage in the structure, such as between 0 and 20kW, and/or in small increments such as 10 W increments.

In some embodiments, calibration device 180 can include a voltage sensor285. Voltage sensor 285 can be configured to sense the voltage ofelectrical power infrastructure 160 and/or sense the phase of thevoltage of electrical power infrastructure 160, which can be measuredthrough the connection of electrical plug 184 to electrical powerinfrastructure 160, such as receptacle 164. In various embodiments, thevoltage and/or phase of the voltage sensed by voltage sensor 285 can betransmitted through transceiver 284. In many embodiments, system 100 canuse the phase of the voltage to facilitate calculating real power.

Still referring to FIGS. 1-2, in many embodiments, display device 120can include a power source 223. In some embodiments, power source 223can be a battery or an electrical plug, such as electrical plug 128,which can provide power to display device 120. Electrical plug can beplugged into electrical power infrastructure 160, such as receptacle168. In a number of embodiments, display device 120 can be configured toreceive the output signal from sensing device 110 and/or the voltageinformation from calibration device 180 via transceiver 224. In variousembodiments, display device 120 can send control signals to calibrationdevice 180 via transceiver 224, such as signals to activate load unit281. In various embodiments, transceiver 224 can be a wired or wirelesschannel, and/or can communicate using a communication protocol, such asWi-Fi, ZigBee, Bluetooth, or another suitable protocol.

In some embodiments, display unit 120 can include a processing module225, memory 226, and/or a display 121. In several embodiments,computational unit 120 can be a small form factor display device. Inother embodiments, computational unit 120 can be a personal computer(PC). In various embodiments, display 121 can be configured to displayinformation, such as power usage, and can be a monitor, a touchscreen,an liquid crystal display (LCD), or another suitable display. in variousembodiments, display 121 can show the result of the techniques describedherein to an end-user in a structure, such as a home.

In a number of embodiments, processing module 225 can be one or moreprocessing units, such as the MSP430 microcontroller manufactured byTexas Instruments, Inc. In another embodiment, processing module 225 canbe a digital signal processor such as the TMS320VC5505 digital signalprocessor manufactured by Texas Instruments, Inc. or a Blackfin digitalsignal processor manufactured by Analog Devices, Inc.

In some embodiments, processing module 225 can be configured to usecurrent measurements from sensing device 110 to determine a calibrationof sensing device 110 and determine electrical power usage in electricalpower infrastructure 160 of the structure, such as the electricalcurrent and/or electrical power of main electrical power conductors 193,194, and 195. In some examples, processing module 225 can execute one ormore modules of computer instructions stored in memory 226, such asneural network module 222, transfer function module 229, phase anglemodule 228, and/or power consumption module 227, described below ingreater detail. Memory 226 can be one or more non-transitory datastorage elements.

Turning ahead in the drawings, FIG. 3 illustrates a block diagram anexemplary system 300, according to a second embodiment. System 300 ismerely exemplary and is not limited to the embodiments presented herein.System 300 can be employed in many different embodiments or examples notspecifically depicted or described herein. System 300 can be similar oridentical to system 100 (FIGS. 1-2), and various components of system300 can be similar or identical to various components of system 100(FIGS. 1-2). In various embodiments, system 300 can include sensingdevice 110 and a calibration device 380. In many embodiments,calibration device 380 can include various elements and/or performvarious functionalities of calibration device 180 (FIGS. 1-2) anddisplay device 120 (FIGS. 1-2). For example, calibration device 380 caninclude load unit 281, load control until 283, voltage sensor 285, andelectrical plug 282 from calibration device 180 (FIGS. 1-2), and caninclude processing module 225, memory 226, display 121, neural networkmodule 222, transfer function module 229, phase angle module 228, andpower consumption module 227 of display device 120 (FIGS. 1-2). Invarious embodiments, two or more elements of calibration device 180(FIGS. 1-2) and display device 120 (FIGS. 1-2) can be combined as asingle element in calibration device 380. For example, transceiver 284(FIG. 2) can be combined in transceiver 224 in calibration device 380.As another example, power source 223 in display device 120 (FIG. 2) canbe combined in electrical plug 282 of calibration device 380.

Referring to FIGS. 1-3, magnetic field sensors 111-114 can each sense amagnetic field generated from main electrical power conductors 193, 194,and/or 195 underlying circuit breaker panel 190, and can generate anoutput signal representing the magnetic field. The output signalgenerated by magnetic field sensor 111 is denoted herein as S₁. Theoutput signal generated by magnetic field sensor 112 is denoted hereinas S₂. The output signal generated by magnetic field sensor 113 isdenoted herein as S₃. The output signal generated by magnetic fieldsensor 114 is denoted herein as S₄. To convert the sensed magnetic fieldto current, system 100 and/or system 300 can use a transfer function,which can, given the sensed magnetic flux, determine what the underlyingcurrent flow in the main leg is that induces the flux. System 100 and/orsystem 300 can infer the transfer function using calibration device 180(FIGS. 1-2) and/or calibration device 380 (FIG. 3), which can draw aknown amount of current by powering a resistive load in load unit 281 ata given time while the magnetic field sensors (e.g., 111-114) sense thechange occurring in the magnetic field due to that current draw.

In many embodiments, system 100 and/or system 300 can generate atransfer function, which can be used to convert these magnetic fieldssignals into current waveforms flowing through each of main electricalpower conductors 193, 194, and/or 195. In order to predict real power,system 100 and/or system 300 can determine both the root-mean-squared(RMS) value of the current waveform and phase angle between the currentand voltage waveform, rather than merely the RMS value of the current.

Creating a transfer function to compute the current waveform given themagnetic flux can be non-trivial because of various challenges posed byfundamental characteristics of circuit breaker panel 190 and the sensedmagnetic field. A first challenge can be stray magnetic flux. Inaddition to main electrical power conductors 193, 194, and/or 195,circuit breaker panel 190 also consists of other electrical wires goingthrough the individual circuit breakers (e.g., 161, 165). There are alsowires passing around the main lines and each of those can radiate fairamount of magnetic field depending on the current flowing through them.Magnetic field sensor 111-114 can sense the magnetic field radiated fromall these sources. In order to use determine only the magnetic fieldradiated by the main lines, the magnetic fields radiated by thesurrounding wires should be eliminated during the prediction by thetransfer function identifying only the magnetic flux radiated from mainelectrical power conductors 193, 194, and/or 195.

A second challenge can be the position of sensing device 110. The amountof magnetic field sensed by magnetic field sensors 111-114 can depend onthe distance between magnetic field sensors 111-114 and main electricalpower conductors 193, 194, and/or 195. In order for system 100 and/orsystem 300 to be a placement invariant system that allows sensing device110 to be placed on any position of the breaker panel, the transferfunction should be able to accommodate any distance between magneticfield sensors 111-114 and main electrical power conductors 193, 194,and/or 195.

A third challenge can be isolating the magnetic flux of each of mainelectrical power conductors 193, 194, and/or 195. Current flowingthrough each of main electrical power conductors 193, 194, and/or 195can contribute to the sensed magnetic field of each of magnetic fieldsensors 111-114. Yet sensor 110 does not know a priori how much of themagnetic field generated by each of main electrical power conductors193, 194, and/or 195 contributes to each of the magnetic fields sensedby magnetic field sensors 111-114. As shown in FIG. 1, magnetic fieldsensor 111 (the leftmost sensor) and magnetic field sensor 114 (therightmost sensor) are likely influenced mostly by the leftmost leg (mainelectrical power conductor 193) and the rightmost leg (main electricalpower conductor 195), respectively. But the ratio of influence isunknown a priori. For magnetic field sensors 112-113 (the two middlesensors), the scenario can be even more unpredictable. The transferfunction should be able to figure out the ratio by which each of mainelectrical power conductors 193, 194, and/or 195 influences each ofmagnetic field sensors 111-114.

A fourth challenge can be uncertainty in the wiring of the circuitbreaker panel (e.g., circuit breaker panel 190). Despite guidelinespromulgated by NEMA and NEC, the internal wiring of breaker panelsvaries significantly depending on various factors, such as the skilland/or experience of the electrician who installed it. The transferfunction should be able to work with any breaker panel with any type ofwiring.

Because these challenges can vary across different breaker panels, thesame amount of electrical load can induce different amounts of magneticfield in different panels. Moreover, even in the same panel with thesame positioning of sensing device 110, the relationship between theamount of electrical load and the magnetic field can depend on theexisting magnetic field inside circuit breaker panel 190 as a whole. Forexample, the baseline current through one of main electrical powerconductors 193, 194, or 195 can be I₁ and a positive change of I_(ch)amount can result in a positive change of S_(ch1) in S₁. If the baselinecurrent changes to I₂, the same positive I_(ch) change can cause adifferent amount of change S_(ch2). Depending on how the magnetic fieldsradiated from different wires and their constructive or destructiveinterference, the value of S_(ch2) can even be negative despite apositive I_(ch) value.

Accordingly, the relationship between the electrical current in mainelectrical power conductors 193, 194, and/or 195, and the magnetic fieldsensed by magnetic field sensors 111-114 can be nonlinear and/or candepend on the existing baseline magnetic field and/or the presence ofother magnetic fields. To accommodate this variability and nonlinearity,system 100 and/or system 300 can create multiple polynomial equationsfor each “state” of the breaker panel. By defining the state in terms ofmagnetic flux, system 100 and/or system 300 can build a function foreach state. Such a problem can be well suited for machine-learningtechniques that learn a function with given states as inputs. Forexample, system 100 and/or system 300 can use a neural network model,which can essentially learn a polynomial function to predict output frominput.

In a number of embodiments, neural network module 222 can construct aneural network model using load unit 281. As described above,calibration device can apply various loads, such as cycling through aseries of 25 W, 100 W, 200 W, and 300 W loads. Just before thecalibrator turns on a load, neural network module 222 can start trackingthe values measured by sensing device 110. Turning the load on causes achange in the total current and, thus, the magnetic flux. This change inmagnetic flux can be recorded by system 100 and/or system 300. For eachcalibrator action (e.g., turning on or turning off a load through loadunit 281), neural network module 222 can construct a training instancefor the neural network. The structure of such an instance is shown inTable 1. The first eight columns of every row can be input features ofthe learning algorithm. The ninth column can be the output value thatthe algorithm will try to learn. The neural network can consist of oneinput layer, one output layer, and two hidden layers having five neuronsin each of the layers. In many embodiments, neural network module 222can use a conventional neural-network machine-learning algorithm withthe inputs and outputs described herein to learn the function.

TABLE 1 S_(1p) S_(1c) S_(2p) S_(2c) S_(3p) S_(3c) S_(4p) S_(4c) I_(ch)

In Table 1, S_(1p), S_(2p), S_(3p), and S_(4p) can be the RMS values ofthe four output signals generated by magnetic field sensors 111-114,respectively, before the calibrator turns on the load. S_(1c), S_(2c),S_(3c), and S_(4c) can be the RMS values of the four output signalsgenerated by magnetic field sensors 111-114, respectively, after thecalibrator turns on the load. I_(ch) can be the amount of current thecalibrator added to a leg (one of main electrical power conductors 193,194, or 195) when it is plugged in. Because the relationship betweenmagnetic flux change and electrical current change can depend on theexisting magnetic flux present in circuit breaker panel 190, in manyembodiments neural network module 222 can use both the previous and thecurrent magnetic flux as input features instead of merely using thechange of flux.

In a number of embodiments, calibration device 180 can turn on each loadof load unit 281 for five seconds. After five seconds, calibrationdevice 180 can turn off the load of load unit 281, and neural networkmodule 222 can captures a similar event and calculate a similar traininginstance for the turn-off event. In many embodiments, system 100 and/orsystem 300 does not have access to the absolute value of the currentgoing through each of main electrical power conductors 193, 194, and/or195. In several embodiments, the only a priori information can be theamount of current change calibration unit 180 causes in main electricalpower conductors 193, 194, and/or 195. Consequently, in many embodimentsthe neural network model can be trained to predict only the change incurrent value, not the absolute current value.

To predict the absolute current waveform in each of main electricalpower conductors 193, 194, and/or 195, transfer function module 229 canuse a geometric translation technique that can leverage the predictionmodel and natural electrical activities in a home to create a transferfunction that converts sensor values to current waveform. For the sakeof simplicity, the description of the technique below uses only one,instead of four, magnetic field sensor (e.g., 111-114), and only asingle leg (one of main electrical power conductors 193, 194, and/or195).

At first, system 100 (FIGS. 1-2) and/or system 300 (FIG. 3) can createan initial transfer function using the collected calibration datagenerated by neural network module 222 (FIGS. 2-3). The transferfunction can work for only a small range of magnetic field values, whichcan be limited to the range of loads the calibration device (e.g. 180(FIGS. 1-2), 380 (FIG. 3)) can provide. This range can be stored as acalibrated region, while keeping track of the present magnetic sensorvalues. As the magnetic sensor values change over time as appliances areused in the structure, system 100 (FIGS. 1-2) and/or system 300 (FIG. 3)can use the calibration device (e.g. 180 (FIGS. 1-2), 380 (FIG. 3)) tocalibrate un-calibrated regions, by the calibration device pulling asmall load. The difference in the observed magnetic field signal at thatlevel can be used to update the transfer function.

In many embodiments, when system 100 and/or system 300 begins tocalibrate sensor 110, the only information known is the current RMSmagnetic field (S_(k)) measured by each magnetic field sensor.Calibration device 180 can initially draw a series of 100 W, 200 W, and300 W loads (3 times each) on top of this current magnetic field. Hencethe field value can change and the system can keep track of the maximumvalue of the sensor (S_(k+1)). Based on these three loads being repeatedthree times, neural network module 222 can save nine calibration eventsfrom S_(k) to S_(k+1). For each event, there can be two traininginstances (e.g., one for turning the load on (“on event”) and one forturning the load off (“off-event”)), as described above. As such, neuralnetwork module 222 can gather eighteen training instances from sensorvalue of S_(k) to S_(k+1) and use these instances to train the neuralnetwork model described earlier.

Turning ahead in the drawings, FIG. 4 illustrates an exemplary graph ofa function (F) 400, which can be derived by placing a predicted function(F_(k)) 401 in a region 403 of function (F) 400. In many embodiments,the predicted function (F_(k)) 401 that can convert magnetic fieldchange value from S_(k) to S_(k+1) to current value I_(ch). By trainingthe neural network model, neural network module 222 (FIGS. 2-3) candetermine predicted function 401 (F_(k)), which can convert the magneticfield change value from S_(k) to S_(k+1) to the current change valueI_(ch). In order to find function (F) 400, which can convert anymagnetic field value S to absolute current value I, predicted function401 (F_(k)) can be placed into an appropriate position of F. Becausesystem 100 (FIGS. 1-2) and/or system 300 (FIG. 3) does not know theabsolute value of I, transfer function module 229 (FIGS. 2-3) can assumea random y-axis value R and place predicted function 401 on function (F)400 at region 403 of (S_(k), R). In many embodiments, system 100 (FIGS.1-2) and/or system 300 (FIG. 3) does not know how the function (F) 400looks like at a region 402 from 0 to S_(k). Therefore function (F) 400can be extrapolated at region 402 from (0, 0) to (S_(k), R). Becauseregion 402 of function (F) 400 is extrapolated, it can do a poor job intranslating S to I.

Turning ahead in the drawings, FIG. 5 illustrates an exemplary graph ofa function (F) 500. FIG. 6 illustrates an exemplary graph of a function(F) 600. FIG. 7 illustrates an exemplary graph of a function (F) 700. Inmany embodiments, function (F) 500, function (F) 600, and/or function(F) 700 can be a further refinement of function (F) 400 (FIG. 4), basedon additional calibration sequences. In many embodiments, in order tofurther determine extrapolated region 402 (FIG. 4), transfer functionmodule 229 (FIGS. 2-3) can wait until the value of S, as measured by themagnetic field sensor (e.g., 111-114 (FIGS. 1-3) falls below S_(k) intoposition 402 (FIG. 4), at which point the calibration process can bereinitiated. As described above, neural network module 222 (FIGS. 2-3)can determine a new function (F_(j)) based on the values measured in thecalibration sequence, which can converts magnetic field values fromS_(j) to S_(j+1), where S_(j)<S_(k). For cases in which S_(k)<S_(j+1),transfer function module 229 (FIGS. 2-3) can combine F_(j) with F_(k)and create a new region 502 of function (F) 500 that covers from S_(j)to S_(k+1), as shown in FIG. 5. Otherwise, in cases in whichS_(k)>S_(j+1), transfer function module 229 (FIGS. 2-3) can place F_(j)in a region 602 (which covers the range from S_(j) to S_(j+1)), whichcan be separate from F_(k) in region 604 (which covers the range S_(k)to S_(k+1)).

As shown in FIG. 5, transfer function module 229 (FIGS. 2-3) can createan extrapolated region 501 from 0 to S_(j). If the measured magneticfield ever falls below S_(j), such as during the night when most of theappliances are off, neural network module 222 (FIGS. 2-3) can initiate anew calibration cycle for the new region and transfer function module229 (FIGS. 2-3) can further regine and/or recreate function (F) 500 fromthe new position to S_(k+1).

As shown in FIG. 6, transfer function module 229 (FIGS. 2-3) can createextrapolated region 501 from 0 to S_(j) and an extrapolated region 603from S_(j+1) to S_(k). In case the measured value is S_(m) whereS_(j+1)<S_(m)<S_(k), system 100 can trigger calibration device 180(FIGS. 1-2) and/or system 300 can trigger calibration device 380 (FIG.3) again so that neural network module 222 (FIGS. 2-3) and transferfunction module 229 (FIGS. 2-3) can update function (F) 600 from S_(m)to S_(k+1). After updating function (F) 600, transfer function module229 (FIGS. 2-3) can extrapolate from S_(j+1) to S_(m), as the transferfunction module 229 (FIGS. 2-3) already has a function from S_(j) toS_(j+1), as shown in region 602.

As shown in FIG. 7, if more appliances within the structure are turnedon and the sensor value (S_(n)) exceeds S_(k+1), system 100 can triggercalibration device 180 (FIGS. 1-2) and/or system 300 can triggercalibration device 380 (FIG. 3) again so that neural network module 222(FIGS. 2-3) and transfer function module 229 (FIGS. 2-3) can updatefunction (F) 500 (FIG. 6) to create function (F) 700, as updated in aregion 704 from S_(n) to S_(n+1). After updating function (F) 700,transfer function module 229 (FIGS. 2-3) can extrapolate in a region 703from S_(k+1) to S_(n) to, as transfer function module 229 (FIGS. 2-3)already has a prediction function in region 502 from S_(j) to S_(k+1).As time goes by and more appliances within the structure are turned onand/or off, which can result in additional calibration sequences, theextrapolated regions (e.g., 501, 703) can shrink more and more andtransfer function module 229 (FIGS. 2-3) can refine a better translationfunction (e.g., function 400, 500, 600, or 700) from S to I. As system100 (FIG. 1) and/or system 300 (FIG. 3) runs in a house, it canadvantageously capture the usual electrical activities in the house,which can increasingly provide it with a wide range of sensor values tolearn from using neural network module 222 (FIGS. 2-3). As moreappliances are turned on and off, system 100 (FIGS. 1-2) and/or system300 (FIG. 3) can calibrate for more and more ranges and the predictedtransfer function (e.g., function 400 (FIG. 4), function 500 (FIG. 5),function 600 (FIG. 6), or function 700 (FIG. 7)) can become increasinglyaccurate.

Turning ahead in the drawings, FIG. 8 (top) illustrates an exemplarygraph of magnetic flux for output signals S₁, S₂, S₃, and S₄ generatedby magnetic field sensors 111-114 (FIGS. 1-3) and FIG. 8 (bottom)illustrates an exemplary graph of a predicted current waveform I throughone leg (e.g., one of main electrical power conductors 193, 194, and/or195). Once system 100 (FIGS. 1-2) and/or system 300 (FIG. 3) starts, itcan create a function F (e.g., function 400 (FIG. 4), function 500 (FIG.5), function 600 (FIG. 6), or function 700 (FIG. 7)), which can takesthe four magnetic field values (S₁, S₂, S₃, S₄) measured by magneticfield sensors 111-114 (FIGS. 1-3) and can translate them into currentwaveform I. FIG. 8 (top) shows a sample input and FIG. 8 (bottom) showsa corresponding sample output of the prediction function F.

As shown in FIG. 8, system 100 (FIGS. 1-2) and/or system 300 (FIG. 3)can predict the raw current waveform flowing through each leg (e.g., oneof main electrical power conductors 193, 194, and/or 195). In otherwords, it can predict both the RMS current (I) and the phase of thecurrent (I), which can be used to calculate the phase angle (θ) betweenthe line voltage and the current (I). Predicting this θ can be relevantfrom an energy monitoring perspective, as it can allow system 100 (FIGS.1-2) and/or system 300 (FIG. 3) to determine the real power, as opposedto apparent power, consumed by the household.

Turning ahead in the drawings, FIG. 9 (top) illustrates an exemplarygraph of a predicted current waveform I and a measured voltage waveform,and FIG. 9 (bottom) illustrates an exemplary graph of magnetic flux foroutput signals S₁, S₂, S₃, and S₄ generated by magnetic field sensors111-114 (FIGS. 1-3) that were used to predict the current waveform I. Inmany embodiments, the voltage waveform can be measured by voltage sensor285 (FIGS. 2-3). To predict phase angle θ, system 100 (FIGS. 1-2) and/orsystem 300 (FIG. 3) can rely on the hypothesis that “any change into thephase of the current waveform will also be reflected into the sensorwaveform.” FIG. 9 shows an example of the validity of the hypothesis. Asshown in FIG. 9 (top), the measured voltage and predicted currentwaveforms are closely in phase with each other (θ is small). Carefulinspection of FIG. 9 (bottom) shows that two of the magnetic waveforms(S₁ and S₄) have the same phase characteristics (zero crossing rise andfall at almost the same timestamps) as the current waveform. In otherwords, the transfer function (e.g., function 400 (FIG. 4), function 500(FIG. 5), function 600 (FIG. 6), or function 700 (FIG. 7)) can beinfluenced more by magnetic field sensors 111 and 114 (FIGS. 1-3) whenpredicting current waveform.

Turning ahead in the drawings, FIG. 10 (top) illustrates an exemplarygraph of a predicted current waveform I and a measured voltage waveform,and FIG. 10 (bottom) illustrates an exemplary graph of magnetic flux foroutput signals S₁, S₂, S₃, and S₄ generated by magnetic field sensors111-114 (FIGS. 1-3) that were used to predict the current waveform I.FIG. 10 (top) shows a different scenario in which the current waveformis lagging the voltage waveform by an angle θ. As shown in FIG. 10(bottom) From the bottom graph, two of the magnetic waveforms (S₁ andS₄) are also following the current waveform. In other words, when thecurrent waveform is phase shifted by angle θ, four sensor waveforms willalso get phase shifted by some angles θ₁, θ₂, θ₃, and θ₄. These anglescan be different from the original phase shift θ. Yet the sensors thatare influenced primarily by the current waveform can have a closer shiftto the angle θ. As such, a difference (θ_(diff)) between the originalshift and the sensed shift can be small.

In many embodiments, the presence of the sheet metal of panel 196(FIG. 1) between of main electrical power conductors 193, 194, and/or195, and magnetic field sensors 111-114 (FIGS. 1-3) can result in thephase difference (θ_(diff)) between the actual current waveform goingthrough main electrical power conductors 193, 194, and/or 195, and themagnetic waveform sensed by magnetic field sensors 111-114 (FIGS. 1-3)becoming a nonlinear function based on the magnetic saturation andpermeability of the material. In a number of embodiments, magnets 117(FIGS. 1-3) surrounding magnetic field sensors 111-114 (FIGS. 1-3) canadvantageously saturate the magnetic field and reduce the nonlinearityeffect. As a result, θ_(diff) can become near constant and the transferfunction (e.g., function 400 (FIG. 4), function 500 (FIG. 5), function600 (FIG. 6), or function 700 (FIG. 7)) can predict the phase angle withgood accuracy.

In several embodiments, phase angle module 228 (FIGS. 2-3) can determinethe phase difference and/or phase angle between the predicted currentusing the transfer function (e.g., function 400 (FIG. 4), function 500(FIG. 5), function 600 (FIG. 6), or function 700 (FIG. 7)) generated bytransfer function module 229 (FIGS. 2-3), and using the voltage measuredby voltage sensor 285 (FIGS. 2-3). The phase angle can be equal to thephase angle of the predicted current minus the phase angle of thevoltage measured using voltage sensor 285 (FIGS. 2-3), which can be usedto determine the phase angle of the voltage across electrical powerinfrastructure 160 (FIG. 1). In several embodiments, the phase angle ofthe predicted current can be calculated in reference to the zero pointcrossing of the measured voltage. In a number of embodiments, powercomputation module 227 (FIGS. 2-3) can determine the real power based onthe phase difference and/or phase angle. For example, the real power canbe equal to the product of RMS values of the current and voltage and thecosine of the phase angle.

Turning ahead in the drawings, FIG. 11 illustrates exemplary graphsshowing a transfer function and its decomposed elements. Mathematically,the transfer function (e.g., function 400 (FIG. 4), function 500 (FIG.5), function 600 (FIG. 6), or function 700 (FIG. 7)) can be expressed asfollows: I=F(S₁, S₂, S₃, S₄). As the function is five-dimensional (fourinputs and one output), it can be challenging to visualize the effect ofeach of the magnetic field sensors (e.g., 111-114 (FIGS. 1-3) on thepredicted current output. The top four plots of FIG. 11 show thepredicted current (I) based on each of a single sensor value (S₁, S₂,S₃, or S₄). For each of the first four plots, one sensor value (S₁, S₂,S₃, or S₄) is varied from 0 microtesla (μT) to 100 μT linearly, keepingall the other sensor values at 0 μT. The bottom plot of FIG. 11 showsthe predicted current (I) based on all four sensor values. The bottomplot assumes all four sensors values increase from 0 μT to 0.05 μT. Thecurrent is measured in amperes (amp).

The plotting in FIG. 11 can be less than ideal, as in actual operationof system 100 (FIGS. 1-2) and/or system 300 (FIG. 3), the current can bepredicted based on different combinations of all sensor values. Yet FIG.11 can provide interesting insight. As an example, after a certain fieldvalue, the predicted current values go down for all the sensor valuesexcept S₁. This phenomenon is observed because of the presence ofmultiple magnetic waveforms inside panel 196 (FIG. 1). As the phases ofthese waveforms are different and they are always changing based on loadcondition, there can be constructive and destructive interferences indifferent locations inside panel 196 (FIG. 1). Depending on the locationin which sensing device 110 (FIGS. 1-3) is placed on circuit breakerpanel 190 (FIG. 1), the magnetic field sensors (e.g., 111-114 (FIGS.1-3) can sense destructive interferences when there is a positive changein the current waveform and can exhibit an inverse relationship betweencurrent and magnetic field.

In the bottom plot of FIG. 11, in which all the sensor values areincreasing, there is a similarity with the topmost plot of FIG. 11 inwhich only S₁ is increasing. Although the predicted current (I) for thethree other sensor values (S₂, S₃, and S₄) is decreasing after a while,the predicted current (I) is always increasing in the bottommost plot ofFIG. 11. Essentially, this behavior means that the transfer function(FIG. 4), function 500 (FIG. 5), function 600 (FIG. 6), or function 700(FIG. 7)) is influenced primarily by S₁. In other words, magnetic fieldsensor 111 (FIGS. 1-3) corresponding to sensor value S₁ can reflect thecurrent waveform more precisely than the other magnetic field sensors112-114 (FIGS. 1-3), which correspond to sensor values S₂, S₃, and S₄,respectively. For example, the neural network model learned by neuralnetwork module 222 (FIGS. 2-3) can increase the coefficient of S₁ morethan S₂, S₃, and S₄. In such a case, the amplitude and phase of thepredicted current can be determined primarily by S₁, which illustrateswhy a machine learning-based approach can be more appropriate for thiskind of problem, as it can be difficult to fit a single polynomial forthese observations.

To validate the techniques described herein, an evaluation was conductedin six different homes and one industrial building. The homes hadtwo-phase wiring systems and the industrial building had a three-phasesystem. Data was collected from one house for a longer period, spanningseven days, and from the other places for a shorter period, spanning twodays. The evaluation show the general applicability of system 100 (FIGS.1-2) and/or system 300 (FIG. 3), and the techniques described herein, toa diverse set of breaker panels (e.g., circuit breaker panel 190 (FIG.1)), as well as the longer-term temporal stability of these techniques.Table 2 shows the summary of the homes used in the evaluation, based onpanel type, style, and size. H1-H6 are the six homes. H1 had the systemdeployed for 7 days. I1 is the industrial building.

TABLE 2 ID Panel type Style/Built/Remodeled Size/Floors H1 Two-phaseApartment/1993/Not 550 square feet (sq. ft.)/1 floor applicable (NA) H2Two-phase House/1972/2002 1250 sq. ft./1 floor H3 Two-phaseApartment/1931/1994  800 sq. ft./1 floor H4 Two-phase House/1960/NA 2220sq. ft./1 floor H5 Two-phase House/1987/NA 1340 sq. ft./1 floor H6Two-phase House/NA/NA 1452 sq. ft./1 floor I1 Three-phaseIndustrial/2003/NA NA

All of the data collection sessions were performed under a naturalisticsetting with the usual home appliances comprising of inductive,resistive, and other complex harmonics appliances. The residents and/oroccupants of the structures were not given any instructions on the useof their electrical appliances or requested to make any changes in theirdaily routines or household tasks. Once installed (e.g., once sensingdevice 110 (FIGS. 1-3) was attached to circuit breaker panel 190 (FIG.1), calibration device 180 (FIGS. 1-2) was plugged into receptacle 164(FIG. 1), and display device 120 (FIGS. 1-2) was plugged into receptacle168 (FIG. 1)), the system ran automatically in the background for theentire data collection session with no human interaction at all.

The system was packaged such that it could be rapidly setup in a home.The sensing device (e.g., sensing device 110 (FIGS. 1-3)) was placed ona breaker panel (e.g., circuit breaker panel 190 (FIG. 1)) usingdouble-sided tape. To collect the ground truth, we installedcommercially available high end transformer-based split-core CTs (99%accurate) inside the breaker panel prior to installing our sensor uniton the outside of the breaker panel. Both the output of the sensingdevice and the output of the CT were collected using the same dataacquisition device (DAQ), specifically National Instruments USB-6259attached to a laptop computer (e.g., display device 120 (FIG. 1)).

Long extension cables were used to bring the receptacles of differentphases branches closer to the laptop. Two calibration devices (e.g., 180(FIGS. 1-2)) were plugged into the receptacles (e.g., 164, 168). Thecalibration devices and the data acquisition device were connected to alaptop. The laptop controlled the calibration device, recorded all thedata from the data acquisition device, and performed all the algorithmicprocessing in real-time. The original and predicted waveforms were alsorecorded for post-experiment analysis. The software portion in thelaptop was written in Matlab.

For each of the deployments, the RMS current value (I_(RMS)) wascalculated in ampere, the RMS line voltage (V_(RMS)) was calculated involts, and phase angle (θ) of the current waveform with respect tovoltage was calculated in degrees every second. These quantities wererecorded both for ground truth current waveform (measured from the CTs)and predicted current waveform (as predicted by the software modules).Finally, the real power consumption (P) was calculated for each of twomain legs every second as follows: P=V_(RMS)×I_(RMS)×cos θ. Duringaccuracy calculation, only the accuracy of the calibrated regions wereconsidered. Yet after a certain time, most regions became calibrated andall of the data were taken into consideration.

The system was installed using two calibration device in each of the twodifferent phases of the house. Based on the calibration data, twodifferent functions F₁ and F₂ for two branch phases P₁ and P₂respectively, were created. During the evaluation, the case of usingjust one calibrator in one of the phases was also considered. Thus, foreach home, the accuracy was calculated for all three possible cases:using just one calibrator in P₁, using just one calibrator in P₂, andusing both calibrators in both phases. During all of the deployments,both calibrators were installed in both of the phases all the time, andboth of the functions F₁ and F₂ for P₁ and P₂, respectively, wererecorded, but the laptop only used F₁ to predict current in both P₁ andP₂ and F₂ to predict current in both P₁ and P₂. Table 3 shows thesummary of all the deployment results.

TABLE 3 Accuracy Using Both Accuracy Using Phase Accuracy Using PhaseDeployment Power Phase Calibration (%) 1 Calibration (%) 2 Calibration(%) time Range I_(RMS) Power I_(RMS) Power I_(RMS) Power ID (hours)(W-W) (A) cosθ (W) (A) cosθ (W) (A) cosθ (W) H1 168 252-4952 98.1 96.896.2 90.3 88.4 89.1 86.7 86.4 86.1 H2 48 396-6840 95.6 97.7 96.7 89.284.7 86.6 91.3 85.9 87.7 H3 48 598-6673 96.9 94.3 95.8 92.9 89.3 90.391.7 88.8 89.3 H4 48  707-12373 97.2 95.3 96.0 90.4 85.5 87.4 85.3 81.084.9 H5 48 441-5567 94.2 93.9 94.0 86.6 84.0 85.7 87.2 82.5 84.7 H6 48311-4110 93.3 90.8 91.2 87.4 82.1 83.1 88.1 86.4 86.7 I1 48 1920-5982 96.8 91.6 95.2 83.1 78.3 80.1 84.3 81.1 82.9 Aggregate 456  252-1237396.0 94.3 95.0 88.5 84.6 86.0 87.8 84.5 86.0

Table 3 shows that, through deployments in six homes and one industrialbuilding, the predicted RMS current and phase angles have an accuracy of96.0% and 94.3%, respectively. Overall, the average accuracy across allthe deployments while using two calibrators is 95.0% in real-worldnaturalistic energy use. This shows the robustness of our system inpredicting real power across different breaker panels and placement inreal environment with natural electrical activities. The evaluation alsoconfirmed that the system does not rely on the precision of placement ofthe sensing device. In all of the deployments in the evaluation,depending on the structure of the breaker, the accuracy remainedunaffected by the placement of the sensing device.

Turning ahead in the drawings, FIG. 12 illustrates a view of system 100attached to circuit breaker 190 and electrical power infrastructure 160,showing various sensor placement positions. An experiment was conductedin a controlled environment to further analyze the positioning effect onaccuracy. The controlled environment was used in order to prevent theaccuracy from being affected by different electrical conditions. For theexperiment, a sensing device (e.g., sensing device 110) was placed at 6different locations on a breaker panel (e.g., circuit breaker panel190), including position 1271, position 1272, position 1273, position1274, position 1275, and position 1276. For each of the positions (e.g.,1271-1276), a controlled environment was maintained, as described below.

First, the environment was made to be electrically quiet, with noappliances being turned on or off, after which the baseline powerconsumption (C) of the environment was measured. Next, a 300 W load fromthe calibration device (e.g., 180) was turned on 3 times on top of thebaseline to create a prediction function that works from C W to C+300 W.Next, a 100 W fan was turned on, which brought the baseline to C+100 W.Based on the prediction function that worked from C to C+300 W, theprediction function was expected to perform well for the 100 W fan loadcondition.

After 10 seconds, the 100 W fan load was turned off. Next, a 1300 Wheater was turned on, and the same procedure as described above usingthe 300 W load from the calibration device was used to calibrate thesystem from C+1300 W to C+1600 W. Finally, keeping the 1300 W load on, a500 W rice cooker was turned on, and the same calibration procedure wasused to calibrate from C+1800 W to C+2100 W.

TABLE 4 Position Accuracy (%) 171 97.7 172 98.2 173 97.6 174 97.1 17597.4 176 96.3

Table 4 shows the accuracy in each of the 6 positions. For all positionson the breaker panel, the minimum accuracy was 96.3%, with an averageaccuracy of 97.4%, despite the non-ideal position of the sensing device.This experiment confirmed that the approach described herein worksindependent of sensor position on the breaker panel with high accuracy.

The longer that a system (e.g., system 100 (FIGS. 1-2, 12) and/or system300 (FIG. 3)) runs in a structure, the wider and more accurate thecalibrated regions become. As the transfer function covers more of thepower consumption range of the structure, the calibration frequencyfurther decreases as well. As such, as long as the power consumption inthe structure resides within the calibrated region, the calibrationdevice (e.g., 180) can be turned off with little effect on the overallaccuracy. An additional experiment was perform to verify thishypothesis.

First, the system was run for 24 hours in a home with all the existingappliances and the system calibrated for the region between 247 W-5344W, yielding an overall accuracy of 95.7%. Next, the calibration devicewas turned off, and four new appliances were turned on. The four newappliances were two bulbs of 125 W and 250 W, one fan of 100 W, and aheater of 700 W, each having different load profiles than were usedduring calibration. The appliances were turned on both individually andin combination while keeping the total power consumption within thecalibrated range. This experiment resulted in a small drop in accuracyto 94.2%. This experiment confirmed that even with the calibrationdevice turned off and new appliances being introduced, the overallaccuracy does not significantly deteriorate as long as the consumptionresides within the previously calibrated region. Moreover, thisexperiment also shows that the generated function does not overfit basedon existing appliances. Rather, it can be flexible enough to work withany new appliance as long as the total consumption does not exceed thecalibrated region.

Low power factor loads such as Switch Mode Power Supply (SMPS)appliances can consume power in higher order harmonics of 60 Hz power.The sampling rate used in the experiments was 9.6 kHz, in which thesensing device can capture harmonic contents up to 4.8 kHz (79harmonics). As such, the sensing device can be similar to a CT, in thatboth can need to be sampled at a high enough sampling rate to capture 60Hz harmonics. The difference is that the sensing device (e.g., sensingdevice 110 (FIGS. 1-3, 12) described herein does not need to be around alive wire, and hence can be much easier to install. Because the sensingdevice is not attached to a live wire, it can need to learn the transferfunction to convert the sensed magnetic field to the actual currentflow.

An experiment was performed to understand how much power in a home canbe attributed to the harmonics in order to design a sensing device thatcould considerably reduce the engineering costs by reducing the samplingrate and data bandwidth requirements. For a typical home over a periodof a month, it was determined that the 60 Hz harmonics contribute toonly 0.15% of total power, which suggests that a simpler sensing systemcould be designed when only total power measurement is of concern to theend user, albeit with an approximately 0.15% loss in accuracy.

To further investigate the accuracy of the system described herein withlow power factor appliances, a seven-day deployment was performed in oneof the homes with a bias towards SMPS appliances (two televisions, twolaptops, an array of CFL bulbs, an active air conditioner, and frequentuse of a microwave). This resulted in a small drop of I_(RMS) and cos θaccuracy, yielding 95.9% and 90.0%, respectively. The total poweraccuracy was 92.2%. This experiment further confirms that the systemalso works with low power factor loads.

Proceeding to the next drawing, FIG. 13 illustrates a flow chart for anembodiment of a method 1300 of sensing electrical power being providedto a structure using a sensing device, a calibration device, and one ormore processing modules. Method 1300 is merely exemplary and is notlimited to the embodiments presented herein. Method 1300 can be employedin many different embodiments or examples not specifically depicted ordescribed herein. In some embodiments, the procedures, the processes,and/or the activities of method 1300 can be performed in the orderpresented. In other embodiments, the procedures, the processes, and/orthe activities of the method 1300 can be performed in any other suitableorder. In still other embodiments, one or more of the procedures, theprocesses, and/or the activities in method 1300 can be combined orskipped.

In some embodiments, the sensing device can be similar or identical tosensing device 110 (FIGS. 1-3, 12). In a number of embodiments, thecalibration device can be similar or identical to calibration device 180(FIGS. 1-2, 12) or calibration device 380 (FIG. 3). In many embodiments,the one or more processing modules can be similar or identical toprocessing module 225 (FIGS. 2-3). In various embodiments, the sensingdevice can include one or more magnetic field sensors. The magneticfield sensors can be similar or identical to magnetic field sensors111-114 (FIGS. 1-3). In a number of embodiments, the sensing device canbe attached to a panel of a circuit breaker box. For example, the panelcan be similar or identical to panel 196 (FIGS. 1, 12), and the circuitbreaker box can be similar or identical to circuit breaker panel 190(FIGS. 1, 12). In various embodiments, the panel of the circuit breakerbox can overlie at least a part of one or more main electrical powersupply lines for an electrical power infrastructure of a structure. Forexample, the one or more main electrical power supply lines can besimilar or identical to main electrical power conductors 193, 194,and/or 195 (FIG. 1). The electrical power infrastructure can be similaror identical to electrical power infrastructure 160 (FIGS. 1, 12). Insome embodiments, the calibration device can include a load unit. Theload unit can be similar or identical to load unit 281 (FIGS. 2-3). Invarious embodiments, the calibration device can be electrically coupledto the electrical power infrastructure of the structure. For example,the calibration device can be electrical coupled such as via electricalplug 282 (FIGS. 2-3).

Referring to FIG. 13, method 1300 can include a block 1301 ofautomatically calibrating the sensing device by determining a firsttransfer function in a piecewise manner based on a plurality of ordinarypower consumption changes in the structure over a first time period. Insome embodiments, the first transfer function can be similar oridentical to function 400 (FIG. 4), function 500 (FIG. 5), function 600(FIG. 6), and/or function 700 (FIG. 7)). In a number of embodiments, theordinary power consumption changes can be the turning on or turning offof appliances in the home that is not dependent on or affected bycalibration of the sensing device. In various embodiments, block 1301can include iteratively predicting the first transfer function by theone or more processing modules based at least in part on (a) measuredregions of magnetic flux measured by the sensing device before and afterthe load unit of the calibration device applies a load and (b)extrapolated regions outside the measured regions. The measured regionscan be similar or identical to regions 403 (FIG. 4), 502 (FIGS. 5, 7),602 (FIG. 6), 604 (FIG. 6), and/or 704 (FIG. 7). The extrapolatedregions can be similar or identical to regions 403 (FIG. 4), 501 (FIGS.5-7), 603 (FIG. 6), and/or 703 (FIG. 7). In various embodiments block1301 can be perform at least in part by neural network module 222 (FIGS.2-3) and/or transfer function module 229 (FIGS. 2-3). In someembodiments, the first time period can be at least 48 hours. In otherembodiments, the first time period can be greater than or less than 48hours. For example, the first time period can be the amount of timerequired to calibrate at least a predetermined percentage of the firsttransfer function over the range of possible usage in the structure.

In certain embodiments, the load unit of the calibration device can beconfigured to provide a maximum load of no more than 1000 W. In otherembodiments, the load unit of the calibration device can be configuredto provide a maximum load of no more than 300 W. In yet otherembodiments, the load unit of the calibration device can be configuredto provide a maximum load of no more than another suitable wattage. Invarious embodiments, the load unit of the calibration unit can beconfigured to draw no more than four discrete loads. For example, theload unit can be configured to draw 25 W, 100 W, 200 W, and 300 W loads.In other embodiments, the load unit of the calibration unit can beconfigured to draw no more than one, two, three, five, six, seven,eight, or another suitable number of discrete loads.

In various embodiments, the load unit of the calibration device can beconfigured to draw a range of loads. For example, the load unit can beconfigured in some cases to draw a range of loads of 300 W, such as from0 W to 300 W. In some embodiments, after the sensing device iscalibrated, the one or more processing modules can be configured to usethe sensing device to determine a range of power consumptionmeasurements. For example, the sensing device can be calibrated in somecases to sense a range of power consumption measurements of 10 kW, suchas from 0 W to 10 kW. In a number of embodiments, the range of loads canbe less than the range of power consumption measurements. In someembodiments, the range of loads can be less than 20% of the range ofpower consumption measurements. In other embodiments, the range of loadscan be less than 10% of the range of power consumption measurements. Inyet other embodiments, the range of loads can be less than 5% or anothersuitable percentage of the range of power consumption measurements.

In some embodiments, block 1301 of automatically calibrating the sensingdevice by determining a first transfer function in a piecewise mannerbased on a plurality of ordinary power consumption changes in thestructure over a first time period can include a block 1302 of traininga neural network model upon sensing a triggering event corresponding tothe plurality of ordinary power consumption changes to determine asecond transfer function converting a magnetic field change measurementto a current change value. In many embodiments, the neural network modelcan be trained by neural network module 222 (FIGS. 2-3), as describedabove. In several embodiments, the triggering events can based on theordinary power consumption changes which result in a sensed magneticfield within one or the extrapolated regions. In various embodiments,the second transfer function can be similar or identical to predictedfunction 401 (FIG. 4). In many embodiments, the second transfer functioncan be learned by neural network module 222 (FIGS. 2-3). In a number ofembodiments, the block 1302 can be implemented as shown in FIG. 14 anddescribed below.

In some embodiments, block 1301 of automatically calibrating the sensingdevice by determining a first transfer function in a piecewise mannerbased on a plurality of ordinary power consumption changes in thestructure over a first time period can include a block 1303 of updatingthe first transfer function with the one or more processing modulesbased at least in part on the second function. In many embodiments, thefirst transfer function can be configured to convert a magnetic fieldmeasurement to an absolute current value. In several embodiments,transfer function module 229 (FIGS. 2-3) can update the first transferfunction based at least in part on the second function, such as shown inFIGS. 4-7 and described above.

In some embodiments, method 1300 can further include a block 1304 ofdetermining a power consumption measurement using the one or moreprocessing modules based on one or more output signals of the sensingdevice and the first transfer function. The one or more output signalsof the sensing device can be similar or identical to output signals S₁,S₂, S₃, and/or S₄, as measured by magnetic field sensors 111-114 (FIGS.1-3), respectively. In some embodiments, block 1304 can includedetermining a phase difference between an electrical current flowing inthe one or more main electrical power supply lines and a voltage of theone or more main electrical power supply lines. In some embodiments, thephase difference can be calculated by phase angle module 228 (FIGS.2-3), as described above. In some embodiments, block 1304 can includedetermining a real power usage based at least in part on the phasedifference. In many embodiments, the power consumption measurementand/or real power usage can be computed by power computation module 227(FIGS. 2-3), as described above.

Turning to the next drawing, FIG. 14 illustrates a flow chart for anembodiment of a block 1302 of training the neural network model uponsensing the triggering event. Block 1302 is merely exemplary and is notlimited to the embodiments presented herein. Block 1302 can be employedin many different embodiments or examples not specifically depicted ordescribed herein. In some embodiments, the procedures, the processes,and/or the activities of block 1302 can be performed in the orderpresented. In other embodiments, the procedures, the processes, and/orthe activities of the block 1302 can be performed in any other suitableorder. In still other embodiments, one or more of the procedures, theprocesses, and/or the activities in block 1302 can be combined orskipped.

Referring to FIG. 14, block 1302 can include a block 1401 of uponsensing a first change in magnetic flux corresponding to the triggeringevent, measuring one or more first magnetic field values from thesensing device. In many embodiments, the first change in magnetic fluxcorresponding to the triggering event can be sensed by the magneticfield sensors. In a number of embodiments, the magnetic field sensorscan measure the first magnetic field values, which can be similar oridentical to S_(1p), S_(2p), S_(3p), and/or S_(4p), as described above.In various embodiments, the triggering events can include ordinary powerconsumption changes in which the one or more first magnetic field valuesmeasured from the sensing device correspond to an extrapolated region ofthe first transfer function. For example, the extrapolated regions canbe similar or identical to regions 403 (FIG. 4), 501 (FIGS. 5-7), 603(FIG. 6), and/or 703 (FIG. 7).

In some embodiments, block 1302 additionally can include a block 1402 ofapplying a predetermined load of the load unit of the calibration deviceto the electrical power infrastructure, the predetermined load drawing afirst current amount. The first current amount can be similar oridentical to I_(ch), as described above.

In various embodiments, block 1302 further can include a block 1403 ofsensing one or more second magnetic field values of the sensing devicewhile the predetermined load is applied to the electrical powerinfrastructure. In a number of embodiments, the magnetic field sensorscan measure the second magnetic field values, which can be similar oridentical to S_(1c), S_(2c), S_(3c), and/or S_(4c).

In some embodiments, block 1302 additionally can include a block 1404 ofusing the one or more processing modules to train the neural networkmodel using the one or more first magnetic field values and the one ormore second magnetic field values as an input layer of the neuralnetwork model, and the first current amount as an output layer of theneural network model. For example, the neural network model can betrained using neural network module 222, as described above.

Turning to the next drawing, FIG. 15 illustrates a flow chart for anembodiment of a method 1500 of sensing electrical power being providedto a structure using a sensing device, a calibration device, and one ormore processing modules. Method 1500 is merely exemplary and is notlimited to the embodiments presented herein. Method 1500 can be employedin many different embodiments or examples not specifically depicted ordescribed herein. In some embodiments, the procedures, the processes,and/or the activities of method 1500 can be performed in the orderpresented. In other embodiments, the procedures, the processes, and/orthe activities of the method 1500 can be performed in any other suitableorder. In still other embodiments, one or more of the procedures, theprocesses, and/or the activities in method 1500 can be combined orskipped.

In some embodiments, the sensing device can be similar or identical tosensing device 110 (FIGS. 1-3, 12). In a number of embodiments, thecalibration device can be similar or identical to calibration device 180(FIGS. 1-2, 12) or calibration device 380 (FIG. 3). In many embodiments,the one or more processing modules can be similar or identical toprocessing module 225 (FIGS. 2-3). In a number of embodiments, thesensing device can be attached to a panel of a circuit breaker box. Forexample, the panel can be similar or identical to panel 196 (FIGS. 1,12), and the circuit breaker box can be similar or identical to circuitbreaker panel 190 (FIGS. 1, 12). In various embodiments, the panel ofthe circuit breaker box can overlie at least a part of one or more mainelectrical power supply lines for an electrical power infrastructure ofa structure. For example, the one or more main electrical power supplylines can be similar or identical to main electrical power conductors193, 194, and/or 195 (FIG. 1). The electrical power infrastructure canbe similar or identical to electrical power infrastructure 160 (FIGS. 1,12). In some embodiments, the calibration device can include a loadunit. The load unit can be similar or identical to load unit 281 (FIGS.2-3).

Referring to FIG. 15, method 1500 can include a block 1501 ofdetermining a current flowing in the one or more main electrical powersupply lines based at least in part on one or more output signals of thesensing device. in various embodiments, the output signals can besimilar or identical to output signals S₁, S₂, S₃, and/or S₄, asmeasured by magnetic field sensors 111-114 (FIGS. 1-3), respectively. Invarious embodiments, the sensing device can include one or more magneticfield sensors. The magnetic field sensors can be similar or identical tomagnetic field sensors 111-114 (FIGS. 1-3). In several embodiments, themagnetic field sensors can be configured to measure a magnetic fluxproduced by at least a part of the one or more main electrical powersupply lines and generate the one or more output signals of the sensingdevice based on the magnetic flux measured by the sensing device. In anumber of embodiments, the sensing device can be devoid of beingelectrically or physically coupled to the one or more main electricalpower supply lines. For example, the sensing device can be uncoupled,whether directly or indirectly, to any of the main electrical powersupply lines.

In some embodiments, method 1500 additionally can include a block 1502of determining a phase difference between the current flowing in the oneor more main electrical power supply lines and a voltage of the one ormore main electrical power supply lines measured by the calibrationunit. In various embodiments, the calibration device can be electricallycoupled to the electrical power infrastructure of the structure. Forexample, the calibration device can be electrical coupled such as viaelectrical plug 282 (FIGS. 2-3). In some embodiments, block 1502 caninclude determining a phase of the current based on a phase of themagnetic flux measured by the sensing device. For example, the phase canbe calculated by phase angle module 228 (FIGS. 2-3), as described above.

In some embodiments, method 1500 can optionally include a block 1503 ofdetermining a determining a real power usage based at least in part onthe phase difference. In many embodiments, the real power usage can becomputed by power computation module 227 (FIGS. 2-3), as describedabove.

Turning ahead in the drawings, FIG. 16 illustrates an exemplaryembodiment of computer system 1600, all of which or a portion of whichcan be suitable for implementing the techniques described above. As anexample, a different or separate one of chassis 1602 (and its internalcomponents) can be suitable for implementing the techniques describedabove. Furthermore, one or more elements of computer system 1600 (e.g.,refreshing monitor 1606, keyboard 1604, and/or mouse 1610, etc.) canalso be appropriate for implementing the techniques described above.Computer system 1600 comprises chassis 1602 containing one or morecircuit boards (not shown), Universal Serial Bus (USB) port 1612,Compact Disc Read-Only Memory (CD-ROM) and/or Digital Video Disc (DVD)drive 1616, and hard drive 1614. A representative block diagram of theelements included on the circuit boards inside chassis 1602 is shown inFIG. 17. Central processing unit (CPU) 1710 in FIG. 17 is coupled tosystem bus 1714 in FIG. 17. In various embodiments, the architecture ofCPU 1710 can be compliant with any of a variety of commerciallydistributed architecture families.

Continuing with FIG. 17, system bus 1714 also is coupled to memorystorage unit 1708, where memory storage unit 1708 comprises both readonly memory (ROM) and random access memory (RAM). Non-volatile portionsof memory storage unit 1708 or the ROM can be encoded with a boot codesequence suitable for restoring computer system 1600 (FIG. 16) to afunctional state after a system reset. In addition, memory storage unit1708 can comprise microcode such as a Basic Input-Output System (BIOS).In some examples, the one or more memory storage units of the variousembodiments disclosed herein can comprise memory storage unit 1708, aUSB-equipped electronic device, such as, an external memory storage unit(not shown) coupled to universal serial bus (USB) port 1612 (FIGS.16-17), hard drive 1614 (FIGS. 16-17), and/or CD-ROM or DVD drive 1616(FIGS. 16-17). In the same or different examples, the one or more memorystorage units of the various embodiments disclosed herein can comprisean operating system, which can be a software program that manages thehardware and software resources of a computer and/or a computer network.The operating system can perform basic tasks such as, for example,controlling and allocating memory, prioritizing the processing ofinstructions, controlling input and output devices, facilitatingnetworking, and managing files. Some examples of common operatingsystems can comprise Microsoft® Windows® operating system (OS), Mac® OS,UNIX® OS, and Linux® OS.

As used herein, “processor” and/or “processing module” means any type ofcomputational circuit, such as but not limited to a microprocessor, amicrocontroller, a controller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, agraphics processor, a digital signal processor, or any other type ofprocessor or processing circuit capable of performing the desiredfunctions. In some examples, the one or more processors of the variousembodiments disclosed herein can comprise CPU 1710.

In the depicted embodiment of FIG. 17, various I/O devices such as diskcontroller 1704, graphics adapter 1724, video controller 1702, keyboardadapter 1726, mouse adapter 1706, network adapter 1720, and other I/Odevices 1722 can be coupled to system bus 1714. Keyboard adapter 1726and mouse adapter 1706 are coupled to keyboard 1604 (FIGS. 16-17) andmouse 1610 (FIGS. 16-17), respectively, of computer system 1600 (FIG.16). While graphics adapter 1724 and video controller 1702 are indicatedas distinct units in FIG. 17, video controller 1702 can be integratedinto graphics adapter 1724, or vice versa in other embodiments. Videocontroller 1702 is suitable for refreshing monitor 1606 (FIGS. 16-17) todisplay images on a screen 1608 (FIG. 16) of computer system 1600 (FIG.16). Disk controller 1704 can control hard drive 1614 (FIGS. 16-17), USBport 1612 (FIGS. 16-17), and CD-ROM drive 1616 (FIGS. 16-17). In otherembodiments, distinct units can be used to control each of these devicesseparately.

In some embodiments, network adapter 1720 can comprise and/or beimplemented as a WNIC (wireless network interface controller) card (notshown) plugged or coupled to an expansion port (not shown) in computersystem 1600 (FIG. 16). In other embodiments, the WNIC card can be awireless network card built into computer system 1600 (FIG. 16). Awireless network adapter can be built into computer system 1600 byhaving wireless communication capabilities integrated into themotherboard chipset (not shown), or implemented via one or morededicated wireless communication chips (not shown), connected through aPCI (peripheral component interconnector) or a PCI express bus ofcomputer system 1600 (FIG. 16) or USB port 1612 (FIG. 16). In otherembodiments, network adapter 1720 can comprise and/or be implemented asa wired network interface controller card (not shown).

Although many other components of computer system 1600 (FIG. 16) are notshown, such components and their interconnection are well known to thoseof ordinary skill in the art. Accordingly, further details concerningthe construction and composition of computer system 1600 and the circuitboards inside chassis 1602 (FIG. 16) are not discussed herein.

When computer system 1600 in FIG. 16 is running, program instructionsstored on a USB-equipped electronic device connected to USB port 1612,on a CD-ROM or DVD in CD-ROM and/or DVD drive 1616, on hard drive 1614,or in memory storage unit 1708 (FIG. 17) are executed by CPU 1710 (FIG.17). A portion of the program instructions, stored on these devices, canbe suitable for carrying out at least part of the techniques describedabove.

Although computer system 1600 is illustrated as a desktop computer inFIG. 16, there can be examples where computer system 1600 may take adifferent form factor while still having functional elements similar tothose described for computer system 1600. In some embodiments, computersystem 1600 may comprise a single computer, a single server, or acluster or collection of computers or servers, or a cloud of computersor servers. Typically, a cluster or collection of servers can be usedwhen the demand on computer system 1600 exceeds the reasonablecapability of a single server or computer. In certain embodiments,computer system 1600 may comprise a portable computer, such as a laptopcomputer. In certain other embodiments, computer system 1600 maycomprise a mobile device, such as a smart phone. In certain additionalembodiments, computer system 1600 may comprise an embedded system.

In many embodiments, the systems and methods described herein present asignificant improvement over past work on non-contact end-userdeployable sensor for real time whole home power consumption. Thetechniques described can allow users to place a single device consistingof magnetic pickups on the outside of a power or breaker panel to inferwhole home power consumption without the need for professionalinstallation of current transformers. This approach advantageously doesnot require precise placement on the breaker panel, which was a keyrequirement in previous approaches. By being independent of sensorplacement, the techniques described here can greatly reduces theinstallation effort required by end users.

This approach can beneficially be enabled through a self-calibrationtechnique using a neural network that dynamically learns the transferfunction despite the placement of the sensor and the construction of thebreaker panel itself. This approach advantageously can have the abilityto infer real-time absolute real power consumption in a structure, basedon the capability of predicting the absolute current waveform, unlikepast solutions that have only been able to capture apparent power.

In many embodiments, the self-calibrating techniques described hereincan dynamically generates a multi-order transfer function between themagnetic sensors and the actual current across the entire range of poweruse in the home. Instead of mathematically modeling the transferfunction a priori, the systems and methods described herein can use alearning approach to generate this transfer function for each home,which can be less susceptible to differences in breaker panel design andconstruction. Further, this approach can remove the need for preciseplacement of the sensor because it can takes into account “interference”from any branch circuits. Because of the in situ dynamic model, thesystems described herein can be not limited to perfect placement ofsensors.

In many embodiments, the techniques described here can beneficially usea calibration device with a much smaller range (0-300 W) than pastapproaches by leveraging the use of the natural electrical activity of ahome throughout the day as a part of the self-calibration sequence. Thenatural household electrical activities can advantageously be leveragedthroughout the day to generate a transfer function for the entire rangeof power use in the home.

In several embodiments, this approach can advantageously have theability to predict the phase angle between the current and voltage toinfer true power, which is equivalent to predicting the waveform itselfand not just the magnitude. In many embodiments, the phase angle canadvantageously be calculated using a single set of magnetic sensors. Ina number of embodiments, the self-calibrating approach beneficially doesnot require precise placement of the sensor on the breaker panel anduses the actual power use throughout a day for calibration. In someembodiments, a neural network-based learning approach can beneficiallydynamically generate a multi-order magnetic sensor transfer function.

In many embodiments, the high accuracy of the systems, method,techniques, and approaches described herein can be ideal for manyapplications such as energy disaggregation, activity inference, andeco-feedback while reducing the barrier to entry by greatly simplifyingthe installation process. Further, the approximately 5% maximum error inthese approaches is much better than the commonly used IEEE C57.13C-class CTs (rated for <=10% error). Moreover, their rated error forsuch CTs is generally at a low current level. At higher current(e.g., >2 A, which is usually expected in a whole-borne scenario), theerror rates are much higher. Very expensive CTs with 1-2% error are usedin specialized applications such as precision current measurements. But,as described above, CTs require installation with access to thecurrent-carrying conductors. The systems and method described herein canadvantageously allow researchers in the energy disaggregation communityto easily access power data in a home without the need for professionalinstallation.

In several embodiments, the approach described herein can beneficiallybe used to automatically calibrate a stick-on real-power meter, whichcan be installed by the homeowner without manual calibration. To assessthe energy viability of using the self-calibration approach, the energydissipated by the calibrator across all of our deployments wascalculated to be 0.181 kWh on average per home for the calibration toconverge on the full transfer function. In some embodiments, the systemcan be calibrated each time the consumption falls into an un-calibratedregion. In other embodiments, the system can be calibrated only when theconsumption falls outside a threshold region.

Although the invention has been described with reference to specificembodiments, it will be understood by those skilled in the art thatvarious changes may be made without departing from the spirit or scopeof the invention. Accordingly, the disclosure of embodiments of theinvention is intended to be illustrative of the scope of the inventionand is not intended to be limiting. It is intended that the scope of theinvention shall be limited only to the extent required by the appendedclaims. For example, to one of ordinary skill in the art, it will bereadily apparent that various elements of FIGS. 1-17 may be modified,combined, and/or interchanged, and that the foregoing discussion ofcertain of these embodiments does not necessarily represent a completedescription of all possible embodiments. As another example, one or moreof the procedures, processes, or activities of FIGS. 13-15 may includedifferent procedures, processes, and/or activities and be performed inmany different orders.

All elements claimed in any particular claim are essential to theembodiment claimed in that particular claim. Consequently, replacementof one or more claimed elements constitutes reconstruction and notrepair. Additionally, benefits, other advantages, and solutions toproblems have been described with regard to specific embodiments. Thebenefits, advantages, solutions to problems, and any element or elementsthat may cause any benefit, advantage, or solution to occur or becomemore pronounced, however, are not to be construed as critical, required,or essential features or elements of any or all of the claims, unlesssuch benefits, advantages, solutions, or elements are expressly statedin such claim.

Moreover, embodiments and limitations disclosed herein are not dedicatedto the public under the doctrine of dedication if the embodiments and/orlimitations: (1) are not expressly claimed in the claims; and (2) are orare potentially equivalents of express elements and/or limitations inthe claims under the doctrine of equivalents.

What is claimed is:
 1. A system for sensing electrical power usage in anelectrical power infrastructure of a structure, the structure comprisinga circuit breaker box and one or more main electrical power supply linesfor the electrical power infrastructure of the structure, the systemcomprising: a sensing device configured to be attached to a panel of thecircuit breaker box overlying at least part of the one or more mainelectrical power supply lines, the sensing device comprising one or moremagnetic field sensors; a calibration device configured to beelectrically coupled to the electrical power infrastructure of thestructure, the calibration device comprising a load unit; and one ormore processing modules configured to receive one or more output signalsfrom the sensing device, wherein: the sensing device is devoid of beingelectrically or physically coupled to the one or more main electricalpower supply lines or the electrical power infrastructure when thesensing device is attached to the panel; the one or more processingmodules are further configured to determine the electrical power usagewhen the sensing device is coupled at any location over the panel, basedat least in part on the one or more output signals received from thesensing device; the load unit of the calibration device is configured todraw a range of loads; the one or more processing modules are configuredto use the sensing device to determine a range of the electrical powerusage; and the range of loads is less than the range of the electricalpower usage.
 2. The system of claim 1, wherein: the one or moreprocessing modules are further configured to determine the electricalpower usage when the sensing device is devoid of being located over theone or more main electrical power supply lines, is devoid of beinglocated over one or more branch electrical power lines that areelectrically coupled to the one or more main electrical power supplylines, and is devoid of being located over one or more branch circuitbreakers that are electrically coupled to the one or more branchelectrical power lines.
 3. The system of claim 1, wherein: the one ormore processing modules are further configured to determine theelectrical power usage when the sensing device is devoid of beinglocated at a predetermined location over the panel.
 4. The system ofclaim 1, wherein: the load unit of the calibration device is configuredto provide a maximum load of no more than 1000 watts.
 5. The system ofclaim 1, wherein: the load unit of the calibration device is configuredto provide a maximum load of no more than 300 watts.
 6. The system ofclaim 1, wherein: the one or more processing modules are furtherconfigured to determine a phase difference between an electrical currentflowing in the one or more main electrical power supply lines and avoltage of the one or more main electrical power supply lines.
 7. Thesystem of claim 6, wherein: the one or more processing modules arefurther configured to determine a real power usage based at least inpart on the phase difference.
 8. The system of claim 7, wherein: thesensing device further comprises one or more magnets surrounding the oneor more magnetic field sensors, the one or more magnets configured tosaturate a magnetic field induced by the panel.
 9. The system of claim1, wherein: the load unit of the calibration device is configured todraw no more than four discrete loads.
 10. The system of claim 1,wherein: the range of loads is less than 20% of the range of theelectrical power usage.
 11. A method of providing a system for sensingelectrical power usage in an electrical power infrastructure of astructure, the structure comprising a circuit breaker box and one ormore main electrical power supply lines for the electrical powerinfrastructure of the structure, the method comprising: providing asensing device configured to be attached to a panel of the circuitbreaker box overlying at least part of the one or more main electricalpower supply lines, the sensing device comprising one or more magneticfield sensors; providing a calibration device configured to beelectrically coupled to the electrical power infrastructure of thestructure, the calibration device comprising a load unit; and providingone or more processing modules configured to receive one or more outputsignals from the sensing device, wherein: the sensing device is devoidof being electrically or physically coupled to the one or more mainelectrical power supply lines or the electrical power infrastructurewhen the sensing device is attached to the panel; the one or moreprocessing modules are further configured to determine the electricalpower usage when the sensing device is coupled at any location over thepanel, based at least in part on the one or more output signals receivedfrom the sensing device; the load unit of the calibration device isconfigured to draw a range of loads; the one or more processing modulesare configured to use the sensing device to determine a range of theelectrical power usage; and the range of loads is less than the range ofthe electrical power usage.
 12. The method of claim 11, wherein:providing the one or more processing modules comprises providing the oneor more processing modules to be further configured to determine theelectrical power usage when the sensing device is devoid of beinglocated over the one or more main electrical power supply lines, isdevoid of being located over one or more branch electrical power linesthat are electrically coupled to the one or more main electrical powersupply lines, and is devoid of being located over one or more branchcircuit breakers that are electrically coupled to the one or more branchelectrical power lines.
 13. The method of claim 11, wherein: providingthe one or more processing modules comprises providing the one or moreprocessing modules to be further configured to determine the electricalpower usage when the sensing device is devoid of being located at apredetermined location over the panel.
 14. The method of claim 11,wherein: providing the calibration device comprises providing the loadunit of the calibration device to be configured to provide a maximumload of no more than 1000 watts.
 15. The method of claim 11, wherein:providing the calibration device comprises providing the load unit ofthe calibration device to be configured to provide a maximum load of nomore than 300 watts.
 16. The method of claim 11, wherein: providing theone or more processing modules comprises providing the one or moreprocessing modules to be further configured to determine a phasedifference between an electrical current flowing in the one or more mainelectrical power supply lines and a voltage of the one or more mainelectrical power supply lines.
 17. The method of claim 16, wherein:providing the one or more processing modules comprises providing the oneor more processing modules to be further configured to determine a realpower usage based at least in part on the phase difference.
 18. Themethod of claim 17, wherein: providing the sensing device furthercomprises providing one or more magnets surrounding the one or moremagnetic field sensors, the one or more magnets configured to saturate amagnetic field induced by the panel.
 19. The method of claim 11,wherein: providing the calibration device comprises providing the loadunit of the calibration device to be configured to draw no more thanfour discrete loads.
 20. The method of claim 11, wherein: the range ofloads is less than 20% of the range of the electrical power usage.